No time for a Masters Degree?

You can get an official Texas A&M certificate in as little as 4 semesters.  Take courses that are tailored to your exact needs.  We have courses in Bayesian Methods, Biostatistics, Spatial Statistics, Time Series, Regression and many more.

See the courses below.

Already have a Master's Degree?

Complement your current Master’s Degree with our wide range of courses available online.  This includes courses in Sampling, Bayesian Methods, Spatial Statistics, Biostatistics and more.

See the course list below.

Necessary steps to receiving a certificate

  • Be admitted to the university.  Apply now

  • Take at least 12 semester credit hours from the Texas A&M University catalogue listing of statistics graduate courses. The specific courses will be chosen by the student, with possible consultation with the Director of the Masters Program, as to best meet the students career goals.

  • Have an overall 3.00 GPA for the statistics courses taken towards the certificate as well as at least an overall 3.00 GPA for all courses taken at TAMU.

  • Prepare a 5-10 page document in a professional format (example:  https://typeset.io/formats/taylor-and-francis/journal-of-applied-statistics/1d6e9b6dbe1acfaa7e851d10dae0c542) describing the analysis of a data set. The document should demonstrate, at the least, an application of the statistical methodology learned in the courses taken by the student. The document should contain the following:

    a. Description of the research goal or purposes of the study generating the data set.
    b. Detailed discussion of the data set and include data set in appendix to the document.
    c. Describe the advanced statistical modeling (at least three predictors) and methodology used to analyze the data set.
    d. Detailed analysis of the data set, including tests of hypotheses, confidence intervals, graphs, tables, assumptions, fix of assumptions, etc for all advanced methods.
    e. Discussion of the analysis with detailed conclusions concerning the degree to which the data supports the research hypotheses.

  • The document described above must be emailed to contact@stat.tamu.edu at least 60 days prior to the proposed graduation date.  In the email, state your name, UIN, and courses with grades.  Once everything has been confirmed and approved, you will be sent an email with further steps.

Version control via Git and Github; code profiling; numerical optimization; writing documentation; creation of R packages; case studies of computational challenges based on modern machine learning methods including regularized logistic regression, k-means clustering, sparse regression.  Credit 3.
Additional Information Coming Soon

Efficient uses of existing statistical computer programs (R, Python); generation of random numbers; using and creating functions and subroutines; statistical graphics; programming of simulation studies; and data management issues.  Credit 3.
Additional Information Coming Soon

Planning, execution, and analysis of sampling from finite populations; simple, stratified, multistage, and systematic sampling; ratio estimates. Prerequisite: STAT 601 or 652 or concurrent enrollment in STAT 641. Credit 3.
Additional Information

Multiple, curvilinear, nonlinear, robust, logistic and principal components regression analysis; regression diagnostics, transformations, analysis of covariance. Prerequisite: STAT 601 or 641. Credit 3.
Additional Information

Survey of common tools used by statisticians for high performance computing and big data type problems; shell scripting; HPC clusters; code optimization and vectorization; parallelizing applications using numerical libraries; open MP, MPI and parallel R; data management and revision control using Git; exploration of SQL, survey NOSQL databases; introduction to Python.  Credit 3.
Additional Information *Coming soon*

Introduction to statistical time series analysis; autocorrelation and spectral characteristics of univariate, autoregressive, moving average models; identification, estimation and forecasting. Prerequisite: STAT 601 or 642 and a working knowledge of complex numbers and trigonometry.  Credit 3.
Additional Information

Basic probability theory including distributions of random variables and their expectations. Introduction to the theory of statistical inference from the likelihood point of view including maximum likelihood estimation, confidence intervals, and likelihood ratio tests. Introduction to Bayesian methods. Prerequisite: Three semesters of calculus, including multiple integration and a basic understanding of limits.  Credit 3.
Additional Information

This course is an applied statistical course that emphasizes on implementing financial and economic models with real data. Using the software in particular R for computation and analysis is essential.

Prerequisite: STAT 630, knowledge of vectors and matrices; probability, distributions and moments; maximum likelihood and
(generalized) least squares estimation; confidence intervals, hypothesis tests and linear regressions.  Credit 3.
Additional Information

Exploratory analysis of multivariate data using ordination and clustering techniques; supervised learning methods of predictive modeling; regression and classification; model selection and regularization; resampling methods; nonlinear and tree-based models; error rate estimation; use of R software. Prerequisites: STAT 630, or STAT 610 and STAT 611; MATH 304. Credit 3.
Additional Information Coming Soon

The objective of this applied master’s level course is to introduce students to the Bayesian paradigm for data analysis. Students learn how uncertainty regarding parameters can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution. Students are exposed to foundational principles, but the course emphasizes modeling and computations under the Bayesian paradigm. Prerequisites: STAT 604, STAT 608 and STAT 630.   Credit 3.
Additional Information

Broad overview of data mining, integrating related concepts from machine learning and statistics; exploratory data analysis, pattern mining, clustering and classification; applications to scientific and online data.  Credit 3.
Additional Information *Coming Soon*

An application of the various disciplines in statistics to data analysis, introduction to statistical software; demonstration of interplay between probability models and statistical inference. Prerequisites: Two semesters of Calculus, STAT 604, and STAT 630.
Credit 3.
Additional Information

Design and analysis of experiments; scientific method; graphical displays; analysis of non-conventional designs and experiments involving categorical data. Prerequisites: STAT 641. Credit 3.
Additional Information

Survey of crucial topics in biostatistics; application of regression in biostatistics;analysis of correlated data; logistic and Poisson regression for binary or count data; survival analysis for censored outcomes; design and analysis of clinical trials; sample size calculation by simulation; bootstrap techniques for assessing statistical significance; data analysis using R. Prerequisites: STAT 651, 652, and 659, or equivalent or prior approval of instructor. Credit 3.
Additional Information

An overview of relevant biological concepts and technologies of genomic/proteomic applications; methods to handle, visualize, analyze, and interpret genomic/proteomic data; exploratory data analysis for genomic/proteomic data; data preprocessing and normalization; hypotheses testing; classification and prediction techniques for using genomic/proteomic data to predict disease status. Prerequisites: STAT 604, 651, 652 or equivalent or prior approval of instructor.
Credit 3.
Additional Information

Spatial correlation and its effects; spatial prediction (kriging); spatial regression; analysis of point patterns (tests for randomness and modelling patterns); subsampling methods for spatial data. Prerequisite: STAT 630 and STAT 608 or STAT 601 or equivalent. Credit 3.
Additional Information

Statistics in Research I. (3-0). Credit 3. For graduate students in other disciplines. A non-calculus exposition of the concepts, methods, and usage of statistical data analysis. T-tests, analysis of variance, and linear regression. Prerequisite: MATH 102 or equivalent.  Credit 3.
Additional Information
*Cannot be used towards the Master’s degree.  Can be used towards a certificate.

Continuation of STAT 651. Concepts of experimental design, individual treatment comparisons, randomized blocks and factorial analysis, multiple regression, chi-square tests and a brief introduction to covariance, non-parametric methods, and sample surveys. Prerequisite: STAT 651.   Credit 3.
Additional Information
*Cannot be used towards the Master’s degree.  Can be used towards a certificate.

The analysis of messy and complex data sets using analysis of variance, analysis of covariance and regression analysis. Transformations; regression diagnostics; nonlinear, robust, logistic and principal components regression; structural equations. Prerequisite: STAT 642 or 652. Credit 3.
Additional Information

Applied Analytics Using SAS Enterprise Miner:  Introduction to data mining and will demonstrate the procedures; Optimal prediction decisions; comparing and deploying predictive models; neural networks; constructing and adjusting tree models; the construction and evaluation of multi-stage models. NOTE: For this course, you will be required to have a computer with a Windows OS and good processing power.  Prerequisite: STAT 657 and 659 or instructor approval.  Credit 3.
Additional Information

SAS Programming Essentials . (3-0). Credit 3. Programming with the SAS Data step and procedures to manage and analyze data, SQL programming, and an introduction to the SAS Macro Language. Prerequisites: Graduate standing or approval of the professor.
Additional Information

Introduction to analysis and interpretation of categorical data using ANOVA/regression analogs; includes contingency tables, loglinear models, logistic regression; use of computer software such as SAS, GLIM, SPSSX. Prerequisite: STAT 601 or 652 or 642 or 608 Credit 3.
Additional Information

The course will cover many aspects of semiparametric regression, especially involving generalized linear models such as logistic regression, and including completely nonparametric regression, partially linear models, additive models and grouped data (including longitudinal data). Other topics will be discussed if time permits, including shape constraints, spatial models, robustness and accounting for missing observations. Multiple R packages will be used, including gamlss, nlme, mgcv, quantreg, refund, rstan, VGAM, scar and SCAM. The HRW package that the authors are creating will be used extensively.

Prerequisite: STAT 608. Credit 3.
Additional Information Coming Soon

Course suggestions (non-Calculus)

Applied Statistics - General

STAT 604 – Statistical Computations
STAT 651 – Stats in Research I
STAT 652 – Stats in Research II
STAT 653 – Stats in Research III

Applied Statistics - Biostatistics

STAT 604 – Statistical Computations**
STAT 651 – Stats in Research I
STAT 652 – Stats in Research II
STAT 645 – Biostatistics and Data Analysis
STAT 646 – Statistical Bioinformatics

**STAT 604 should be taken if R programming is not known before taking STAT 645

Course suggestions (Calculus)

Applied Statistics - General

Stat 604 – Computational Stats
Stat 630 –  Math Stats
Stat 641 – Methods I
Stat 642 – Methods II
Stat 657 – Advanced SAS Programming
Stat 659 – Applied Categorical

Applied Statistics - Biostatistics

Stat 630 – Math Stats
Stat 641 – Methods I
Stat 642 – Methods II
Stat 645 – Biostatistics and Data Analysis
Stat 646 – Statistical Bioinformatics